State of Charge Estimation for Both Electric Bus and Passenger Vehicles with Different Battery Types Using Multi-Instance Learning
TL;DR
MIL-LGBM is introduced, a specialized method that integrates Multiple Instance Learning with Light Gradient Boosting Machine to effectively capture the complex nonlinear behavior of battery SoC while maintaining a low computational footprint.
Abstract
State of charge (SoC) estimation for an electric vehicle (EV) is critical for range estimation, preventing overcharging or undercharging, energy management, and battery lifespan. The current studies are typically based on single-instance learning, focus on a specific battery chemistry or a single vehicle type, rely on controlled Lab conditions, and often lack explainability mechanisms. To overcome all these limitations, this paper proposes a more practically applicable and explainable SoC estimation framework that jointly addresses vehicle diversity (bus and passenger EVs), battery chemistry variability (Nickel Cobalt Manganese and Lithium Iron Phosphate), collective battery dynamics, and real-world on-road operational uncertainties within a unified learning architecture. This study introduces MIL-LGBM, a specialized method that integrates Multiple Instance Learning with Light Gradient Boosting Machine to effectively capture the complex nonlinear behavior of battery SoC while maintaining a low computational footprint. An experimental study conducted on a real-world dataset demonstrated that the proposed framework achieved a 0.799 MAE for passenger vehicles across a wide range of on-road driving conditions.